JP2015232855A - Event identity determination method, event identity determination device, and event identity determination program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To appropriately set a threshold value in determining identity of events expressed by a plurality of documents.SOLUTION: Event information that specifies events is stored in an event DB102 of an event identity determination device 100, and document information of electronic documents which is an extraction source of events is stored in a document DB103. A threshold value determination part 104 refers to the DB102, 103 in advance, calculates statistical data from a collection of the event information and a collection of the document information, and determines a threshold value of similarity between electronic documents to store the value in a threshold value storage part 105 in advance. An identity determination part 101 refers to the event DB102 and reads out event information of a subject to be determined. Based on the read-out event information, electronic documents are read out from the document DB103 and similarity between the electronic documents is calculated, the calculated similarity is compared with a threshold value in the threshold value storage part 105, and identity between the electronic documents is determined.

Description

  The present invention relates to an information processing technique for determining the identity of description contents of a plurality of electronic documents (hereinafter referred to as documents).

  As a method for extracting description contents of a document such as social text represented by a blog, that is, a fact described in the document (hereinafter referred to as “event”), a technique for extracting event information from the text has been proposed. Yes.

  For example, Non-Patent Document 1 proposes a method of extracting three sets of a name, a place, and a date / time included in a text by using structural information and storing them as event information. According to such a method, event information described in a social text document such as a blog can be stored and reused.

  However, a method for determining whether or not a plurality of pieces of event information extracted from text express the same event has not been proposed. One possible method in this case is to calculate the similarity between the original documents, and if the documents have similarities of a certain level or more, it is determined that the documents describe the same event and extracted. It is conceivable that the event information is recognized as the same.

  As a method for calculating the similarity between documents, comparison of keyword vectors used in the information search field is well known (see Non-Patent Document 2).

Yoshihiko Nubara, Jun Suzuki, Seiji Amagasaki. Extraction of local event information from texts using structural learning. National Congress of the Japanese Society for Artificial Intelligence 2013 Kita Kenji, Tsuda Kazuhiko, Sasabori Masatomi. Information Retrieval Algorithm. Kyoritsu Shuppan, 2002.

  In social text, the same event is frequently described in a plurality of documents. Therefore, when providing event information extracted from social text, it is necessary to determine the identity of event information in order to aggregate / exclude the same event. In this case, if the information expressing the event, such as name, location, date and time, is the same, it is reasonable to consider that they are the same event.

  However, when extracting the name, location, and date / time of an event from social text, the description is often different for each name, location, and date / time, making it difficult to aggregate the same event described in multiple documents as the same event. There are many things.

  In addition, when a keyword vector is simply used for identity determination as in Non-Patent Document 2, a threshold value for determining the identity must be set, but a reasonable threshold value is set for event information. No way to decide has been proposed.

  The present invention has been made in order to solve such a problem of the prior art, and an object of the present invention is to appropriately set a threshold for determining the identity of events expressed by a plurality of documents.

  The event identity determination method of the present invention calculates statistical data from a set of event information for specifying an event in advance and a set of document information of a document from which the event is extracted, and calculates similarity between documents. Threshold determination step for determining a threshold, and documents are read based on event information to be determined, the similarity between the documents is calculated, and the similarity between the documents is determined by comparing the calculated similarity with the threshold. And an identity determination step.

  The event identity determination device of the present invention calculates statistical data from a set of event information for specifying an event in advance and a set of document information of a document from which an event is extracted, and calculates the similarity between documents. A threshold value determination unit that determines a threshold value, reads a document based on event information to be determined, calculates a similarity between documents, and determines the identity between documents by comparing the calculated similarity with the threshold. And an identity determining unit.

  The present invention can also be configured as a program that causes a computer to function as the event identity determination device. This program can be provided through a network or a recording medium.

  According to the present invention, it is possible to appropriately set a threshold for determining the identity of events expressed by a plurality of documents.

The block diagram of the event identity determination apparatus which concerns on embodiment of this invention. The batch processing flowchart of an identity determination part. The same incremental processing flow diagram. The processing flowchart of a threshold value determination part. A graph of similarity distribution (in steps of 0.01) of document pairs. Explanatory drawing of FIG.

  Hereinafter, an event identity determination device according to an embodiment of the present invention will be described. This event identity determination device uses a feature vector of a document describing an event in the event identity determination. That is, the threshold for determining the identity of an event is optimized based on the similarity using a feature vector.

≪Configuration example≫
A configuration example of the event identity determination device will be described with reference to FIG. This event determination apparatus 100 is mainly used for providing event information extracted from social text documents such as blogs, and determines the identity of events for aggregation and exclusion of the same events.

  The event identity determination device 100 is configured by a computer and includes hardware resources such as a CPU, a main storage device (RAM, ROM, etc.), and an auxiliary storage device (hard disk drive device, “Flash SSD”, etc.).

  As a result of the cooperation between the hardware resource and the software resource, the event identity determination apparatus 100 has an identity determination unit 101, an event DB 102, a document DB 103, a threshold determination unit 104, and a threshold storage unit 105. The DBs 102 and 103 and the storage unit 105 are assumed to be constructed in the storage device.

  Table 1 shows an example of data stored in the event DB 102. The DB 102 stores event information for identifying events. Here, as event information, an event ID for identifying the event, the name of the extracted event, the venue of the event, the date and time of the event, and a non-display flag indicating whether or not the event is not displayed The document ID of the original document that is the extraction source and the update date and time stored in the event DB 102 are recorded in a pair.

  Table 2 shows an example of data stored in the document DB 103. The DB 103 stores document information. Here, as document information, a document ID (for example, URL) that can be referred to from the original document ID of the event DB 102, an update date and time stored in the document DB 103, and a body text are recorded in pairs. The body text may be the text as it is, or text that has been morphologically analyzed in advance.

  After reading the event information stored in the event DB 102, the identity determination unit 101 extracts the document from which the event is extracted from the document DB 103, and calculates the similarity between the extracted documents. After this calculation, the threshold value is read from the threshold value storage unit 105, and the similarity between documents is compared with the threshold value. As a result of this comparison, if the similarity between documents is equal to or greater than a threshold, it is determined that the documents are the same, and the display flag of the event DB 102 is updated.

  The threshold value determination unit 104 determines the threshold value in advance using the stored data of both the DBs 102 and 103. The threshold value determined here is stored in the threshold storage unit 105. Hereinafter, processing contents of the respective units 101 and 104 will be described.

<< Identity determining unit 101 >>
The identity determination of the identity determination unit 101 is executed by batch processing or incremental processing according to the generation method of the DBs 102 and 103. That is, when a document is periodically analyzed and a plurality of event information is simultaneously stored in the event DB 102, batch processing is performed.

  On the other hand, when event extraction is processed at any time using a document as a stream, and only one event information is stored in the event DB 102 at a time, increment processing is performed each time. In addition, when performing an incremental process, a batch process may be performed regularly and an identity determination may be recalculated about all the event information in event DB102.

(1) Batch processing The batch processing of the identity determination unit 101 will be described with reference to FIG. Here, it is assumed that the threshold value is already stored in the threshold value storage unit 105.

  S201, S202: When processing is started, the identity determination unit 101 reads a threshold value from the threshold value storage unit 105 (S201). This threshold is one numerical value.

  Next, event information is read from the event DB 102 (S202). At this time, it is possible to reduce the amount of calculation by selecting and reading only the event information whose non-display flag is “0”.

  S203, S204: The identity determination unit 101 reads out document information from the document DB 103 based on the original document ID of each event information read out in S202 (S203). Thereafter, a feature vector of each document is generated based on the event information and the document information, and the generated feature vector is stored in the intermediate file, that is, the feature vector file 210 of the document (S204).

  S205: The identity determination unit 101 calculates similarity between vectors for all feature vectors stored in the feature vector file 210. If the similarity between the vectors calculated here is equal to or greater than the threshold “θ”, it is determined that the documents handle the same event.

  Here, the events determined to be the same are grouped and called the same event group. One event is selected based on the update date in the same event group, and the non-display flag of the selected event is kept “0”. On the other hand, other events other than the selected event in the same event group are set to “1” or more, the record of the event DB 102 is updated, and the process is terminated.

(2) Increment processing The increment processing of the identity determination unit 101 will be described with reference to FIG. Here, it is assumed that the feature vector of the document has already been calculated for the existing event information in the event DB 102 by batch processing before the processing starts. It is assumed that the feature vector calculated here is stored in advance in the intermediate file, that is, the feature vector file 310.

  S301: First, when processing is started, the identity determination unit 101 reads a threshold value from the threshold value storage unit 105 as in S201. The threshold value read out here is also one numerical value.

  S <b> 302: Next, the identity determination unit 101 reads one latest event information from the event DB 102. If the latest event information, that is, unprocessed event information is not stored at this time, the processing is interrupted until the latest event information can be read.

  S303, S304: The identity determination unit 101 reads document information from the document DB 103 based on the original document ID of each event information read in S302 (S303). Thereafter, a feature vector of each document is generated based on the event information and the document information, and the generated feature vector is stored in the feature vector file 310 (S304).

  S305: The identity determination unit 101 calculates the similarity between all the feature vectors existing in the feature vector file 310 for the feature vector newly stored in S304.

  If the similarity between the vectors is equal to or greater than the threshold “θ”, it is determined that the documents handle the same event. Here, for an event determined to be the same as another event, the event information display flag is set to “1” or more, and the record in the event DB 102 is updated. After this process ends, the process returns to S302.

<< Threshold Determination Unit 104 >>
The processing content of the threshold value determination unit 104 will be described with reference to FIG. When the process is started, the threshold value determination unit 104 reads event information from the event DB 102 (S401). Document information corresponding to the event information is read from the document DB based on the original document ID (S402).

  Statistical data is calculated from the set of event information and the set of document information read in S401 and S402, and a threshold value is determined (S403). The determined threshold value is stored in the threshold value storage unit 105, and the process ends.

≪Specific processing contents≫
(1) Similarity Calculation As an example of similarity calculation (S205, S305) in the identity determination unit 101, similarity calculation using a word set, that is, similarity calculation using a weight vector using a keyword will be described.

  In this similarity calculation, the words contained in the document are treated equally and used as a weight vector for each keyword, and this weight vector is used as the feature vector of the document. As a method for configuring this weight vector, the method described in Non-Patent Document 2 can be employed.

  Specifically, for the document information read in S202 and S302, the text of the document is morphologically analyzed and divided into words to form a weight vector for each word, and the similarity between the vectors is calculated.

  As a method of constructing the weight vector, a feature vector having a concatenation of words in a document text as an element is constructed instead of converting individual words into feature vectors. As a result, a characteristic expression of the event can be a characteristic vector of a form such as “adjective + noun” (for example, “soulful diva” or “with tears with laughter”).

In this case, an index called cosine distance is used for calculation between feature vectors in the compared documents. For example, the similarity of “feature vectors d _i , d _j ” of two documents is given by equation (1).

However, “θ _ij ” represents the angle formed by “d _i , d _j ”, “x · y” represents the inner product of two vectors, and “|| x ||” represents the norm of the vector.

(2) Determination of threshold value When similarity calculation using the above-described keyword weight vector is used, it is necessary to set a similarity threshold value for event identity determination. Here, a method for experimentally obtaining the threshold value of similarity will be described.

  In this method, the similarity between documents is obtained for an event set extracted from a certain amount of document set, and a threshold is determined from the distribution. Here, “10,988” event information randomly selected from the event set extracted from the blog described in “October 2013 to January 2014” is included in the document. Based on the appearance frequency of words, weight vectors were constructed to calculate the similarity.

  FIG. 5 is a graph showing the distribution of similarity of calculation results, with the horizontal axis representing similarity and the vertical axis representing the number of event pairs (logarithm). Here, if the same event is not included, the graph is expected to monotonously decrease toward the right. That is, for document pairs that refer to different events, most of the document pairs are distributed in the vicinity of similarity “0”, and document pairs with high similarity (close to “1”) are less than document pairs with low similarity. Is expected.

  However, as shown in FIG. 6, although it is expected that the similarity of most document pairs is distributed in the vicinity of “0”, the similarity “0.6 to 0.8” is defined as a valley. It was found that the number of document pairs is increasing in the region where the similarity is higher than “0.8”.

  Therefore, in this case, it is only necessary to set a threshold value between the similarities “0.6 to 0.8”, and although there is a possibility that there is a document pair having a similarity degree equal to or higher than the threshold value by chance, There are few, and it is thought that it does not become a problem in practical use.

As a method for setting a threshold from such a distribution, a method using a sliding window can be considered. For example, the similarity is frequencyized in increments of “0.01”, the number of event pairs with similarity “0 to 0.01” is “a ₁ ”, and event pairs with similarity “0.01 to 0.02” are The number is “a ₂ ”, and similarly, the number of event pairs with similarity “(k−0.01) to _k ” is “a _k ” (when the similarity is in increments of 0.01, k = 1, ..., 100).

  At this time, with respect to the window size w, “i” is sequentially increased from “1”, and a point at which the sum of the number of event pairs for each window size starts to increase is set as a threshold value. More precisely, the threshold when the similarity range is divided into “n” is obtained by equation (2). Note that “min” in Equation (2) indicates the minimum value among the elements in the set.

  Alternatively, similarly, the distribution is leveled by the sliding window, and the center of the section with the smallest frequency is set as the threshold value. The threshold value in this case can be obtained by equation (3).

  As described above, according to the event identity determination apparatus 100, a threshold for determining the identity of events expressed by a plurality of documents is experimentally obtained. At this time, it is possible to set a mechanically appropriate threshold value based on the sliding window from the distribution obtained in the experiment.

≪Other ・ Program≫
The present invention is not limited to the above-described embodiment, and can be implemented by being modified within the scope described in each claim. For example, the threshold value determination unit 104 and the threshold value storage unit 105 can be clouded.

  In addition, the present invention may be configured as a document search program that causes a computer to function as a part or all of the units 101 to 105 of the event identity determination apparatus 100. According to this program, it is possible to cause a computer to execute part or all of S201 to S205, S301 to S305, and S401 to S404.

  The program can be provided through a network such as a website or e-mail. The program is stored in a recording medium such as a CD-ROM, DVD-ROM, CD-R, CD-RW, DVD-R, DVD-RW, MO, HDD, BD-ROM, BD-R, or BD-RE. It is also possible to record, save and distribute. This recording medium is read using a recording medium driving device, and the program code itself realizes the processing of the above embodiment, so that the recording medium also constitutes the present invention.

DESCRIPTION OF SYMBOLS 100 ... Event identity determination apparatus 101 ... Identity determination part 102 ... Event DB
103 ... Document DB
104: Threshold value determination unit 105 ... Threshold value storage unit 210, 310 ... Feature vector file (intermediate file)

Claims (7)

An event identity determination method for determining the identity of events described in a plurality of electronic documents by a computer,
Threshold determination for calculating statistical data from a set of event information for specifying the event in advance and a set of document information of the electronic document from which the event is extracted, and determining a threshold of similarity between the electronic documents Steps,
An identity determination step of reading out electronic documents based on event information to be determined, calculating a similarity between the electronic documents, and comparing the calculated similarity with the threshold to determine the identity between the electronic documents; ,
An event identity determination method characterized by comprising: The threshold determination step calculates in advance similarity between documents for a set of electronic documents based on the set of event information;
Graphing the calculated similarity distribution with the horizontal axis being the similarity and the vertical axis being the number of event pairs;
In the graph, a sliding window in which the similarity is frequencyized in arbitrary value increments, and when the frequency is gradually increased, the number of event pairs for each window size has changed from a decreasing trend to an increasing trend. A threshold value step;
The event identity determination method according to claim 1, further comprising: The threshold determination step calculates in advance similarity between documents for a set of electronic documents based on the set of event information;
Graphing the calculated similarity distribution with the horizontal axis being the similarity and the vertical axis being the number of event pairs;
In the graph, after leveling the distribution by a sliding window in which the similarity is frequencyized in arbitrary value increments, the center of the interval with the smallest frequency as the threshold value,
The event identity determination method according to claim 1, further comprising: An event identity determination device for determining the identity of events described in a plurality of electronic documents,
Threshold determination for calculating statistical data from a set of event information for specifying the event in advance and a set of document information of the electronic document from which the event is extracted, and determining a threshold of similarity between the electronic documents And
An identity determination unit that reads out electronic documents based on event information to be determined, calculates a similarity between the electronic documents, and compares the calculated similarity with the threshold to determine the identity between the electronic documents; ,
An event identity determination device comprising: The threshold determination unit calculates a similarity between documents for a set of electronic documents based on the set of event information in advance;
Means for graphing the calculated similarity distribution, with the horizontal axis representing similarity and the vertical axis representing the number of event pairs;
In the graph, a sliding window in which the similarity is frequencyized in arbitrary value increments, and when the frequency is gradually increased, the number of event pairs for each window size has changed from a decreasing trend to an increasing trend. Means for thresholding;
The event identity determination device according to claim 4, further comprising: The threshold determination unit calculates a similarity between documents for a set of electronic documents based on the set of event information in advance;
Means for graphing the calculated similarity distribution, with the horizontal axis representing similarity and the vertical axis representing the number of event pairs;
In the graph, after leveling the distribution with a sliding window in which the similarity is frequencyized in arbitrary value increments, the center of the section with the smallest frequency as the threshold,
The event identity determination device according to claim 4, further comprising:   The event identity determination program which makes a computer function as an event identity determination apparatus of any one of Claims 4-6.

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